Comparar métodos

	Modelo GARCH bayesiano	Modelo GARCH (Predicción de Volatilidad)
Campo	Econometría	Econometría
Familia	Regression model	Regression model
Año de origen	1989–2000	1986
Autor original	Geweke (1989); further developed by Nakatsuma (2000) and Bauwens & Lubrano (1998)	Tim Bollerslev
Tipo	Bayesian volatility model	Conditional volatility model
Fuente seminal	Geweke, J. (1989). Exact predictive densities for linear models with ARCH disturbances. Journal of Econometrics, 40(1), 63–86.	Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31(3), 307–327.
Alias	Bayesian GARCH, BGARCH, GARCH with Bayesian inference, Bayesian volatility model	GARCH, GARCH(1,1), conditional volatility model, GARCH Modeli (Oynaklık Tahmini)
Relacionados	4	5
Resumen	The Bayesian GARCH model combines the GARCH framework for time-varying volatility with Bayesian posterior inference. Instead of maximising a likelihood, it specifies prior distributions for the GARCH parameters and draws from the resulting posterior — typically via Markov chain Monte Carlo (MCMC) — to quantify both point estimates and full uncertainty about volatility dynamics.	The Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model, introduced by Tim Bollerslev in 1986, models the time-varying conditional variance of a financial time series. It captures volatility clustering and the ARCH effect, and is the standard tool for estimating risk and volatility in return series.